初始化项目，由ModelHub XC社区提供模型
Model: AI-ModelScope/speed-synthesis-8b-revisor Source: Original Platform
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README.md
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README.md
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---
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license: mit
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language:
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- en
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base_model:
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- meta-llama/Meta-Llama-3-8B
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pipeline_tag: text-generation
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tags:
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- transformers
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---
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## SPEED-synthesis-7b-senior
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[Little Giants: Synthesizing High-Quality Embedding Data at Scale](https://arxiv.org/pdf/2410.18634.pdf). Haonan Chen, Liang Wang, Nan Yang, Yutao Zhu, Ziliang Zhao, Furu Wei, Zhicheng Dou, arXiv 2024
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This is the data revisor model of SPEED.
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## Usage
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Below is an example to revise s2s data using this revisor.
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The prompts and misc scripts can be found in our [github page](https://github.com/haon-chen/SPEED)
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### Transformers
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```python
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import torch
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import os
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import random
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import numpy as np
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import json
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from torch import Tensor
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from transformers import AutoTokenizer, AutoModelForCausalLM
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from typing import List, Dict, Optional
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from prompts_aligning import get_create_all_revise_data_prompt
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from utils import fix_common_json_errors_and_loads_for_revisor
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LLAMA3_PROMPT = """
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{prompt} [/INST]
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""".strip("\n")
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# Each query must come with a one-sentence instruction that describes the task
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old_prompts = [
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"You have been assigned a text matching task: Match a Stockard Channing movie title with a brief plot description.\n\nYour mission is to write one example for this task in JSON format. The JSON object must contain the following keys:\n- \"input\": a string, a random input specified by the task.\n- \"positive_document\": a string, a relevant document for the \"input\" according to the task.\n\nPlease adhere to the following guidelines:\n- The values of all fields should be in English.\n- Both the \"input\" and \"positive_document\" should be very short (a sentence or a phrase), avoid substantial word overlaps, otherwise the task would be too easy.\n- The \"input\" and \"positive_document\" should be independent of each other.\n\nYour output must always be a JSON object only, do not explain yourself or output anything else. Be creative!"
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]
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old_data = [
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{"input": "Stockard Channing in 'The Business of Strangers', directed by Patrick Stettner.", "positive_document": "In 'The Business of Strangers', Channing stars as a businesswoman who embarks on a ruthless journey, after which she undergoes a drastic change. She faces many challenges while pursuing her goals and eventually comes out stronger."},
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]
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language = 'English'
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prompts = [LLAMA3_PROMPT.format(prompt=get_create_all_revise_data_prompt(prompt=old_prompt, data=json.dumps(data))[1]['content']) for old_prompt in old_prompts for data in old_data]
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tokenizer = AutoTokenizer.from_pretrained('Haon-Chen/speed-synthesis-7b-revisor')
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model = AutoModelForCausalLM.from_pretrained('Haon-Chen/speed-synthesis-7b-revisor')
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model.to("cuda:0")
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tokenizer.pad_token = tokenizer.pad_token or tokenizer.eos_token
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tokenizer.padding_side = "left"
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tokenizer.truncation_side = "left"
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# Tokenize the input texts
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encodes = tokenizer(prompts, padding="longest", add_special_tokens=True, return_tensors="pt")
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input_ids = encodes.input_ids.to(model.device)
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attention_mask = encodes.attention_mask.to(model.device)
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GEN_CONFIG = {"do_sample":True, "temperature": 1.0, "top_p": 1.0, "max_new_tokens": 800}
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output = model.generate(
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input_ids=input_ids,
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attention_mask=attention_mask,
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pad_token_id = tokenizer.eos_token_id,
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**GEN_CONFIG
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)
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output_texts = tokenizer.batch_decode(output, skip_special_tokens=True, clean_up_tokenization_spaces=False)
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batch_results = []
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for i in range(len(output_texts)):
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batch_results.append(output_texts[i][len(prompts[i]):].strip(' '))
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bad_cnt=0
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outputs = []
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for i, result in enumerate(batch_results):
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try:
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content = fix_common_json_errors_and_loads_for_revisor(result)
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revision = content["revision"]
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reason = content["reason"]
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user_query = revision.get("input", "")
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positive_document = revision.get("positive_document", "")
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except:
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bad_cnt+=1
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continue
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out_data = {
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"query": user_query,
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"positives": [positive_document],
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"negatives": [],
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"language": "English",
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"reason": reason,
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}
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outputs.append(out_data)
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print(bad_cnt)
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print(outputs)
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```
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## Citation
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If you find our paper or models helpful, please consider cite as follows:
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```bibtex
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@article{chen2024little,
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title={Little Giants: Synthesizing High-Quality Embedding Data at Scale},
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author={Chen, Haonan and Wang, Liang and Yang, Nan and Zhu, Yutao and Zhao, Ziliang and Wei, Furu and Dou, Zhicheng},
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journal={arXiv preprint arXiv:2410.18634},
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year={2024}
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}
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```
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## Limitations
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30
config.json
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config.json
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{
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"_name_or_path": "meta-llama/Meta-Llama-3-8B",
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"architectures": [
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"LlamaForCausalLM"
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],
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"attention_bias": false,
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"attention_dropout": 0.0,
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"bos_token_id": 128000,
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"eos_token_id": 128001,
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"hidden_act": "silu",
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"hidden_size": 4096,
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"initializer_range": 0.02,
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"intermediate_size": 14336,
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"max_position_embeddings": 8192,
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"mlp_bias": false,
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"model_type": "llama",
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"num_attention_heads": 32,
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"num_hidden_layers": 32,
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"num_key_value_heads": 8,
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"pad_token_id": 128001,
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"pretraining_tp": 1,
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"rms_norm_eps": 1e-05,
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"rope_scaling": null,
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"rope_theta": 500000.0,
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"tie_word_embeddings": false,
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"torch_dtype": "float16",
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"transformers_version": "4.43.3",
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"use_cache": false,
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"vocab_size": 128256
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}
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configuration.json
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configuration.json
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{"framework": "pytorch", "task": "text-generation", "allow_remote": true}
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generation_config.json
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generation_config.json
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{
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"bos_token_id": 128000,
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"do_sample": true,
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"max_length": 4096,
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"temperature": 0.6,
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"top_p": 0.9,
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"transformers_version": "4.43.3"
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}
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"up_proj",
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"o_proj",
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"down_proj"
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"task_type": "CAUSAL_LM",
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"use_dora": false,
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||||
"model.norm.weight": "model-00004-of-00004.safetensors"
|
||||
}
|
||||
}
|
||||
23
special_tokens_map.json
Normal file
23
special_tokens_map.json
Normal file
@@ -0,0 +1,23 @@
|
||||
{
|
||||
"bos_token": {
|
||||
"content": "<|begin_of_text|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
},
|
||||
"eos_token": {
|
||||
"content": "<|end_of_text|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
},
|
||||
"pad_token": {
|
||||
"content": "<|end_of_text|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
}
|
||||
}
|
||||
410563
tokenizer.json
Normal file
410563
tokenizer.json
Normal file
File diff suppressed because it is too large
Load Diff
2062
tokenizer_config.json
Normal file
2062
tokenizer_config.json
Normal file
File diff suppressed because it is too large
Load Diff
Reference in New Issue
Block a user